Bottom Line:
This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP).First of all, some indoor human actions are selected as primitive actions forming a training set.Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Affiliation: School of Software, Dalian University of Technology, Dalian 116620, China. dlutwangyi@dlut.edu.cn.

ABSTRACTWireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

sensors-15-17195-f002: An example of action boundary computation by the LOFs of MIMOs: (a,c,e,g) Action 1; (b,d,f,h) Action 2; (a,b) Two actions’ MIMO subplots with their boundaries denoted by the vertical bars; (c–h) Two actions’ LOF subplots with their boundaries denoted by the vertical bars.

Mentions:
After computing the outliers for every MIMO subplot, we get outlier curves, e.g., in Figure 2c–h, and the next step is to determine the boundary of an action.

sensors-15-17195-f002: An example of action boundary computation by the LOFs of MIMOs: (a,c,e,g) Action 1; (b,d,f,h) Action 2; (a,b) Two actions’ MIMO subplots with their boundaries denoted by the vertical bars; (c–h) Two actions’ LOF subplots with their boundaries denoted by the vertical bars.

Mentions:
After computing the outliers for every MIMO subplot, we get outlier curves, e.g., in Figure 2c–h, and the next step is to determine the boundary of an action.

Bottom Line:
This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP).First of all, some indoor human actions are selected as primitive actions forming a training set.Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Affiliation:
School of Software, Dalian University of Technology, Dalian 116620, China. dlutwangyi@dlut.edu.cn.

ABSTRACTWireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.